A brief screening tool for knee pain in primary care (KNEST). 2. Results from a survey in the general population aged 50 and over
Bibliographic record
Abstract
OBJECTIVE: To use a brief screening tool to identify knee pain (all knee pain, non-chronic and chronic knee pain) and associated health-care use in the general population aged 50 yr and over. METHODS: A cross-sectional survey was mailed to 8995 individuals registered with three general practices in North Staffordshire, UK. The questionnaire included a Knee Pain Screening Tool (KNEST), the Short Form 36 (SF36), demographic questions and, for those who reported knee pain, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). RESULTS: The survey achieved a 77% response. The 12-month period prevalence of all knee pain was 46.8% [95% confidence interval (CI) 45.6%, 48.0%]. Figures for non-chronic knee pain (pain of less than 3 months duration) and chronic knee pain (pain of more than 3 months duration) were 21.5% (95% CI 20.5%, 22.5%) and 25.3% (95% CI 24.3%, 26.4%) respectively. An estimated 6% of the older population had non-chronic but severe knee pain or disability. Thirty-three per cent of all knee pain sufferers had consulted their general practitioner (GP) about their symptom in the last year. This included 34% of those with non-chronic but severe knee pain or disability and 56% of those with chronic and severe knee pain or disability. The use of private treatments or services for knee pain was minimal. A third of those with chronic and severe knee pain or disability had not used any services (including GP) in the last year. CONCLUSIONS: The KNEST is a simple tool for the identification of individuals with knee pain and their health-care use. Focusing only on chronic knee pain will underestimate the total need and demand for health-care in knee pain sufferers in the general older population, as non-chronic as well as chronic knee pain has a significant impact on people's lives and on their use of primary health-care. The KNEST, when combined with the WOMAC, identifies population groups who have potentially diverse health-care needs and who might benefit from effective health-care. These data can be used alongside evidence on effective treatments by service planners when considering needs for the care of older adults in primary care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".